Immutable Database are on the rise

Over the last few years many organizations started pilot projects to use blockchains to attain the immutability of their sensitive data. However, the main disadvantages of blockchains (complexity, cost, limits to performance) have in most cases drawn these organizations to simpler, faster and more cost-effective solutions, chiefly among them immutable databases. 

Immutable database can only add data, but never delete or alter it. There is no change API and the veracity of the stored data is continuously authenticated thru a cryptographic audit. Quickly a number of solutions emerged.

Some commercial, some open source, and some technologies that build on top of versioning backends, such as git. Immutable databases, such as our very own immudb, are a merger of two main concepts:

Merkle Tree Graphs and cryptographic hashing. Both concepts have been around for decades, but combined they create a new and powerful platform to store data, and have mathematical proof that this data has not been altered in any way.  

Merkle Tree data over time

In effect, immutable databases, reverse the concept of Blockchains: whereas in blockchains the distributed ledger authenticates the data, and the client is forced to trust it; in an immutable database ,the client doesn’t trust the database and performs a cryptographic verification of the veracity of the data stored in the database.  

Git (DAG) vs Merkle Tree approach

There is also a new breed of versioning databases, which treat data like source files, on Gitlab or github, for example, and use the branching and versioning available to those platforms. The major drawdown of these versioning databases is that the client cannot use a zero trust approach, and instead is asked to trust these versioning platforms to not knowingly or unknowingly spoil the data. 

Git forms a directed acyclic graph (DAG) of commits identified by these hashes. Compared to Merkle Tree based data structures, the Git is a graph, not a tree. https://www.geeksforgeeks.org/difference-between-graph-and-tree/

DAG data over time – source: https://www.geeksforgeeks.org/difference-between-graph-and-tree/
  • Nodes in a Merkle tree do not have multiple parents. The DAG does.
  • Merkle tree only hashes actual data in its leaf nodes, the Merkle branches after the leaf nodes are hashes of hashes. The Git DAG does not have branches based on hashes of hashes, just nodes with hashes of raw data.
  • The Merkle tree starts with the leaf nodes, and proceeds up the tree, constantly halving the number of branches in each iteration. The Git DAG contracts and expands all the time with branching and merging. Because it is a graph and not a tree.

Use cases for immutable databases are principally located where the data change history and the veracity of data are of paramount importance. For example records of financial transactions, government tax records, industrial supply chain events, and many more. 

The main offerings in this new category of databases are:

immudb

Immudb, our very own open source immutable database. It’s open source, free, monstrously fast, and has advanced features such as Key/Valey and/or SQL interface. Time travel to see data change history, high availability, scale-out support, a web console and much more. 

Amazon QLDB

Amazon QLDB in a commercial offering, similar to our own immudb, however the data is managed by Amazon and it can only be accessed thru the cloud, requiring applications to have access to the Internet. It’s not clear how Amazon stores data in QLDB and the client is responsible to gather the proof information additionally.

Oracle Blockchain Tables

Oracle 21c Blockchain Tables, is a new feature in Oracle’s main database product, which stores data in tables, backed up by Oracle’s internal blockchain. The database must be trusted by the client, and there is no possibility for client authentication of the veracity of the data. Furthermore, tables can still be deleted, and restrictive permissions are required.

CNIL Metrics & Logs

Self-Hosted performance monitoring and compliant log analysis for VMware vSphere, container and much more.

immudb

Built on the fastest immutable ledger technology. Open Source and easy to use and integrate into existing application.

Codenotary Cloud

Trusted CI/CD, SBOM and artifact
protection with cryptographic proof.
One CLI to manage all.

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Use Case - Tamper-resistant Clinical Trials

Goal:

Blockchain PoCs were unsuccessful due to complexity and lack of developers.

Still the goal of data immutability as well as client verification is a crucial. Furthermore, the system needs to be easy to use and operate (allowing backup, maintenance windows aso.).

Implementation:

immudb is running in different datacenters across the globe. All clinical trial information is stored in immudb either as transactions or the pdf documents as a whole.

Having that single source of truth with versioned, timestamped, and cryptographically verifiable records, enables a whole new way of transparency and trust.

Use Case - Finance

Goal:

Store the source data, the decision and the rule base for financial support from governments timestamped, verifiable.

A very important functionality is the ability to compare the historic decision (based on the past rulebase) with the rulebase at a different date. Fully cryptographic verifiable Time Travel queries are required to be able to achieve that comparison.

Implementation:

While the source data, rulebase and the documented decision are stored in verifiable Blobs in immudb, the transaction is stored using the relational layer of immudb.

That allows the use of immudb’s time travel capabilities to retrieve verified historic data and recalculate with the most recent rulebase.

Use Case - eCommerce and NFT marketplace

Goal:

No matter if it’s an eCommerce platform or NFT marketplace, the goals are similar:

  • High amount of transactions (potentially millions a second)
  • Ability to read and write multiple records within one transaction
  • prevent overwrite or updates on transactions
  • comply with regulations (PCI, GDPR, …)


Implementation:

immudb is typically scaled out using Hyperscaler (i. e. AWS, Google Cloud, Microsoft Azure) distributed across the Globe. Auditors are also distributed to track the verification proof over time. Additionally, the shop or marketplace applications store immudb cryptographic state information. That high level of integrity and tamper-evidence while maintaining a very high transaction speed is key for companies to chose immudb.

Use Case - IoT Sensor Data

Goal:

IoT sensor data received by devices collecting environment data needs to be stored locally in a cryptographically verifiable manner until the data is transferred to a central datacenter. The data integrity needs to be verifiable at any given point in time and while in transit.

Implementation:

immudb runs embedded on the IoT device itself and is consistently audited by external probes. The data transfer to audit is minimal and works even with minimum bandwidth and unreliable connections.

Whenever the IoT devices are connected to a high bandwidth, the data transfer happens to a data center (large immudb deployment) and the source and destination date integrity is fully verified.

Use Case - DevOps Evidence

Goal:

CI/CD and application build logs need to be stored auditable and tamper-evident.
A very high Performance is required as the system should not slow down any build process.
Scalability is key as billions of artifacts are expected within the next years.
Next to a possibility of integrity validation, data needs to be retrievable by pipeline job id or digital asset checksum.

Implementation:

As part of the CI/CD audit functionality, data is stored within immudb using the Key/Value functionality. Key is either the CI/CD job id (i. e. Jenkins or GitLab) or the checksum of the resulting build or container image.

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